With the increase in the number of species that have been determined of their genome sequences, so called genome comparison has extensively been performed. Genome comparison aims at finding facts based on gene differences among species, for example, finding genes involved in evolution, finding a collection of genes which are considered to be common to all species, or, on the other hand, studying the nature unique to specific species. The recent development of infrastructures such as biochips (DNA chips) and DNA microarrays has changed the interest in the art of molecular biology from information of interspecies to information of intraspecies, namely coexpression analysis, and broadened the study covering from extraction of information to correlation of information, in addition to the conventional comparison between species.
For example, if an unknown gene has an expression pattern identical to that of a known gene, the unknown gene can be assumed to have a similar function to that of the known gene. Functional meanings of such genes and proteins are studied in the forms of function units or function groups. The interactions between the function units or function groups are also analyzed by correlating with known enzymatic reaction data or metabolism data, or more directly, by knocking out or overreacting a specific gene to eliminate or accelerate expression of the gene to study the direct and indirect influences on gene expression patterns of a whole collection of genes.
One successful case in this field would be the expression analysis of yeast by the group of P. Brown et al. from the Stanford University (Michel B. Eisen et al., Clustering analysis and display of genome-wide expression patterns, Proc. Natl. Acad. Sci. (Dec. 8, 1998); 95(25): pp. 14863–8). They conducted hybridization of genes extracted from a cell in a time series using a DNA microarray, and numerically expressed the expression levels thereof (i.e., the brightness of the hybridized fluorescent signals). Based on the numerically-expressed values, genes having similar expression patterns in their gene cycles (genes having closer expression levels at some point) are clustered together.
Furthermore, experimental results as to an efficacy of a medicine has been reported by The Institute of Medical Science, the University of Tokyo (T. Tsunoda et al., Discrimination of Drug Sensitivity of Cancer Using cDNA Microarray and Multivariate Statistical Analysis: Genome Informatics 1999 (December 1999) pp.227–228, Universal Academy Press Inc.). In the experiment, a normal cell sample and a cancer cell sample which are labeled with fluorescent substances with different colors are subjected to hybridization on a biochip. Then, both of the fluorescent signal intensities are measured.
FIG. 3 is a diagram for illustrating an exemplary method for displaying an expression state of each gene obtained from the experiment. In this display, data of brightness of hybridized fluorescent signals are plotted, where one axis represents brightness of a normal cell and the other brightness of a cancer cell. In order to analyze the data, a ratio of brightness of the cancer cells to that of the normal cell is observed for genes having signals higher than a predetermined intensity, thereby narrowing the number of candidate genes specific to a disease. Specifically, genes belonging to Region A in FIG. 3 (genes that function for the normal cell but for the cancer cell) and genes belonging to Region B (genes that function for the cancer cell but for the normal cell) are particularly sorted. According to such a displaying method, the number of candidate genes that act specific to a particular disease can be narrowed down.
The displaying method shown in FIG. 3 is effective in visually understanding rough difference between properties of genes in different, cells, and is currently used as a general method. According to this method, the number of samples to be compared is limited to two. However, in analyzing functions of genes, there is a demand of analyzing various cells suffering from diseases from various points of view, for example, as to genes specific to one or more diseases, or genes that function only under a normal condition. Therefore, a displaying method that is limited to two types of samples is not always satisfactory.
For example, where three types of cells, Normal cell A, Cell B suffering from Disease P and Cell C suffering from Disease Q are to be compared, experiments should be carried out for each two of them. Therefore, two of the displays like one shown in FIG. 3 are obtained as the experiment results. Specifically, one of the two displays may be for the results of an experiment targeting Normal cell A and Cell B suffering from Disease P, and the other for the results of an experiment targeting Normal cell A and Cell C suffering from Disease Q. Based on the experiment results of one of the cells, other two types of cells can be compared with each other. However, even when the two displays like one shown in FIG. 3 are placed side by side, it is hard to understand the states of gene expressions of these three cells by a brief look.
In general, in order to study expression states of a gene, an experiment using a biochip is carried out at each time point, to understand changes of various genes by displaying expression data of each gene in a time series. FIGS. 9A and 9B are representative graphs. FIG. 9A is a graph showing changes of an expression level of one gene (Gene 1) with time. FIG. 9B is a graph collectively showing changes of multiple genes with time. From the graph shown in FIG. 9B, it can be predicted that in a region (900) enclosed with a circle, Genes 1, 2 and 3 are working in cooperation within a predetermined time segment.
However, it is difficult to apply this graph displaying method to the above-described data obtained by observing the ratios of expression levels among the three types of cells, to roughly understand the states of entire changes as to how the gene expressions are related to each other.
In view of such conventional problems, the present invention has an objective of providing a visual display effective in comparing expression data of multiple gene based on experiment results of one type of cell to understand states of groupings and changes.